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Artificial intelligence has become a powerful force in the financial ecosystem and offers faster, data-driven insights that promise to improve investment, lending and risk management. From AI advisors who personalize both corporate and individual financial strategies to sophisticated trading systems that make data-driven decisions in microseconds, there is much room for growth in the financial AI sector.
But there is one major problem. It’s bias.
Despite providing what appears to be speed, accuracy and objectivity, financial AI systems have the same bias that the industry has been trying to eliminate for decades. For example, according to Lehigh University, Openai’s GPT-4 Turbo Large Language Models (which mimic the AI ​​Mortgage Advisor or Decision System) reflected a specific demographic of applicants in order to earn 120 credit points higher than white applicants to obtain the same approval despite having the same income, credit history and debt levels.
This bias affects not only traditional financial markets, but also decentralized finance and crypto ecosystems. For example, take a look at a market forecasting platform powered by AI. Because their data is based on price history, news sentiment, or social trends, these platforms can sometimes overreact anomalies to the market. Crypto is full of Black Swan events, such as Terra collapse, FTX crashes, or major penalties from regulators.
As a result, these prediction tools can be overly aggressive or overweight social tendencies and chatter, resulting in poor signaling and predictions.
Blockchain rescues from Xai
Due to the limitations and opaque nature of many AI systems, they are unable to fully fulfill their transparency and accountability. Some people call them black boxes because AI models usually have little transparency.
In particular, decisions made by AI tools within the crypto space are usually unexplainable. This makes it difficult for users to understand how decisions are made. The lack of standardized audit protocols for AI systems will result in inconsistent evaluations and potential monitoring of important issues.
Integrating blockchain technology with explanatory AI, or XAI, for short, can address this issue by providing the invariance and transparency that a distributed ledger has.
The Xai model has already attracted attention to ensuring that decision-making processes are both fair and ethical, in addition to being efficient. Blockchain technology can complement Xai’s fairness by creating an immutable record of AI’s decision-making processes and ensuring that all actions are traceable and verifiable. This promotes trust and accountability.
Blockchain works in an unreliable way. This does not mean that technology cannot be trusted, but it does suggest that a third party or central authority is not needed to confirm the decision. Decentralization removes the need for centralized entities to oversee processes thanks to autonomously functioning smart contracts.
When a model changes or outputs decisions, a lack of log and version control can cause trust issues on most AI platforms. Blockchain technology time stamps records and data about the immutable ledger.
Credit scoring company FICO uses blockchain to record AI model decisions, allowing regulators to track how decisions like credit approvals were made. The company won the “Future Tech -Blockchain and Tokenisation” award at the Banking Technology Awards held in London last year.
From theory to practice
Blockchain and decentralized finance protocols offer the opportunity to burn fairness, transparency and accountability in AI models. Traditional financial companies have been struggling.
Combining Xai and on-chain verification can change how decisions are made and trusted in the Web3 ecosystem. For example, using Xai to describe the votes in a decentralized autonomous organization can help users better understand the outcome of their choices. A more advanced utility is to use xai to assess the risk of lending defi protocols.
Mixing Xai with blockchain technology is also a powerful on-chain monitoring and operation detection tool. AI is suitable for analyzing patterns of sandwich attacks, MEV exploitation, or washing transactions. This will help you find market anomalies.
Some Web3 projects are already trying to increase AI transparency. For example, singularitynet focuses on making AI processes auditable. Another platform called Ocean Protocol tracks the origin of data, ensuring reliability and traceability.
Conclusion
At this point, it is just the beginning of the integration of blockchain and AI. Researchers are currently investigating hybrid models that monitor blockchain integrity, Xai clarity, bias detection tools and combine them with potentially modifying systems.
However, technology alone does not fix this. It also requires attention from regulatory authorities, scrutiny from users, and humility from the developers who build these systems. If the 2008 financial crisis taught us anything, it means that blind trust in complex, centralized tools is dangerous.
Most notably, smart doesn’t always mean fairness. As an age of mainstream AI surfaces, users need to look for transparency in addition to efficiency.
